Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data

Ha, Y.-J. J.; Maziec, D.; Markowski, J.; Georges, S. J.; Parmalee, N. L.; Berselli, M.; Coorens, T. H.; Dong, S.; Gardiner, S.; Kalra, D.; Li, D.; Miao, B.; Musunuri, R.; Xue, L.; Yu, Z.; Walker, K.; Anderson, L.; Au, N. Y.; Cibulskis, C.; Doddapaneni, H.; Grochowski, C. M.; Jensen, D. M.; Lindsay, T.; Loy, K.; Narayan, A.; Narzisi, G.; Ou, J.; Pham, M. M.; Runnels, A. M.; Stergachis, A. B.; Sutherlin, L. M.; Wang, T.; Jin, H.; Feng, W. C.; Zhang, Y.; Veit, A. D.; Kim, C. T.; Chun, H.-J. E.; Ardlie, K.; Fulton, R. S.; Germer, S.; Gibbs, R. A.; Marth, G. T.; Bennett, J. T.; Park, P. J.

doi:10.1101/2025.10.13.681545

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bioRxiv · 10.1101/2025.10.13.681545

Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data

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Abstract

Mosaic mutations in normal tissues occur at low variant allele fractions (VAFs), complicating detection. To benchmark strategies, the SMaHT Network created a cell-line mixture (1:49) and produced ultra-deep whole-genome sequencing using short and long reads (five centers, 180-500x each). We assembled a reference of 44,008 mosaic SNVs and 2,059 Indels, cross-validation between platforms to expose limits of short-read analysis. We also partitioned the genome by mappability to examine the impact of genomic context, added a negative reference set, and accounted for culture-derived mutations. When seven institutions applied eleven algorithms to mixture data, call sets were largely discordant across tools and replicates, partly reflecting stochastic presence of low-VAF mutations in biological replicants. For >2% VAF SNVs, sensitivity and precision approached [~]80% at [≥]300x, with little gain from additional sequencing. This work provides a comprehensive framework for reliable detection of low-VAF mutations in non-cancer tissues and a valuable resource for the community.

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Ha, Y.-J. J., Maziec, D., Markowski, J., Georges, S. J., Parmalee, N. L., Berselli, M., Coorens, T. H., Dong, S., Gardiner, S., Kalra, D., Li, D., Miao, B., Musunuri, R., Xue, L., Yu, Z., Walker, K., Anderson, L., Au, N. Y., Cibulskis, C., Doddapaneni, H., Grochowski, C. M., Jensen, D. M., Lindsay, T., Loy, K., Narayan, A., Narzisi, G., Ou, J., Pham, M. M., Runnels, A. M., Stergachis, A. B., Sutherlin, L. M., Wang, T., Jin, H., Feng, W. C., Zhang, Y., Veit, A. D., Kim, C. T., Chun, H.-J. E., Ardlie, K., Fulton, R. S., Germer, S., Gibbs, R. A., Marth, G. T., Bennett, J. T., Park, P. J.. 2025-10-14. Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data. https://doi.org/10.1101/2025.10.13.681545

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